Geekversal is a future-oriented intelligent productivity tool that provides IT practitioners with knowledge search, knowledge content management, and knowledge system construction services.
When people browse web pages online, they may bookmark web pages for easy review the next time they need them. However, the traditional way of bookmarking web pages relies on manual archiving by users. If a user has bookmarked a lot of web pages, the process can become extremely annoying. Users need to remember which category they put the bookmark in and search a dense list when reopening. The situation becomes more complicated when one considers that a web page may belong to multiple categories at the same time. Owing to these limitations, the user's web page collection is usually kept at a very low level. In this era of Google/Baidu, almost all human knowledge is recorded on the Internet, so this limitation restricts the ability of users to record knowledge to a certain extent.
Geekversal redefines the function of web page collection and increases users' ability to record knowledge by tens of thousands of times. Users can add a web page to their 'favourites' with one click and add the web page to their personal knowledge base, just as they store knowledge in the web page in their electronic brain with one click. Using this process, users do not need to think about how to classify the web pages, as this process is performed in the background by Geekversal. Based on natural language processing and knowledge graphs, Geekversal splits and associates web page content. When users need to review related knowledge, they can quickly retrieve and rearrange the bookmarked pages. Geekversal not only displays the original text information, but also learns independently and answers user questions naturally. Geekversal currently prioritizes knowledge construction in the computer field, providing IT practitioners with knowledge search, knowledge content management, and knowledge system construction services to create future-oriented intelligent productivity tools, reducing workload and cost.
Team members
Miss Tang Wan-yee (Hong Kong Baptist University)
Miss Ge Yujie (Hong Kong Baptist University)
Mr Lau Ka-hong (Undergrad, College of Science, CityU)
Mr Cheuk Sze-lon (The Chinese University of Hong Kong)
Mr Jerry Zhang (Georgia Institute of Technology)
Mr Zhang Huibo (Georgia Institute of Technology)
* Person-in-charge
(Info based on the team's application form)
- CityU HK Tech 300 Seed Fund (2022)